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Apache Spark 4 | Comparison with previous version

Discover the core differences between Apache Spark 4.0 and Spark 3.x, including performance improvements, columnar execution, streaming upgrades, and Python UDF enhancements.

By

Jatin

Updated on

June 4, 2025

Apache Spark 4.0 represents a significant evolutionary leap from Spark 3.0, introducing fundamental changes that enhance performance, reliability, and developer experience. This article examines the key technical differences between these major versions, providing insights for engineers planning upgrades and architectural decisions.

Release Overview and Community Impact

Apache Spark 4.0 marks the inaugural release in the 4.x series, representing a major milestone with over 5,100 resolved tickets and contributions from more than 390 developers. This collaborative effort has resulted in substantial improvements across all core components of the Spark ecosystem.

In contrast, Spark 3.0, released in June 2020, focused primarily on performance optimizations, achieving roughly 2x performance improvements over Spark 2.4 in TPC-DS benchmarks, and significantly enhanced Python functionality.

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Core Language and Runtime Changes

ANSI SQL Compliance by Default

Spark 4.0 Enhancement: One of the most significant changes in Spark 4.0 is making ANSI mode the default behavior. The spark.sql.ansi.enabled configuration is now set to true by default, fundamentally changing how Spark handles SQL operations.

Technical Implications:

  • Runtime exceptions are thrown for invalid SQL operations instead of returning null results
  • Stricter type checking and data validation
  • Enhanced data quality assurance through rejection of invalid casts during table insertions
  • Better alignment with standard SQL behavior, improving portability from traditional databases

Spark 3.0 Behavior: Spark 3.0 maintained Hive-compatible behavior by default, often returning null values for invalid operations rather than failing fast with exceptions.

Java Runtime Modernization

Spark 4.0:

  • Java 17 as default runtime with Java 21 support added
  • Modern JVM features and performance optimizations
  • Enhanced garbage collection and memory management capabilities

Spark 3.0:

  • Primarily supported Java 8 and 11
  • Limited access to modern JVM performance features
  • More conservative memory management approaches

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Data Type System Enhancements

VARIANT Data Type Introduction

Spark 4.0 Innovation: The introduction of the VARIANT data type represents a major advancement for handling semi-structured data, particularly JSON. This native data type provides:

  • Efficient storage and processing of JSON documents without schema definition
  • Native JSON operations and querying capabilities
  • Better performance for semi-structured data workloads compared to string-based JSON handling
  • Seamless integration with SQL and DataFrame APIs

Spark 3.0 Limitation: Semi-structured data handling relied primarily on string manipulation and complex parsing operations, leading to performance bottlenecks and more complex code patterns.

String Collation Support

Spark 4.0 introduces comprehensive string collation support, enabling:

  • Locale-specific string comparisons and sorting
  • Case-insensitive operations with proper internationalization support
  • Enhanced text processing capabilities for global applications

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Python Ecosystem Advancements

Python Data Sources

Spark 4.0 Feature: Native Python data source capabilities allow developers to create custom data sources entirely in Python, providing:

  • Simplified integration with Python-native data systems
  • Reduced barrier to entry for Python developers
  • Better ecosystem integration with Python data tools

Polymorphic Python UDTFs

Advanced Function Capabilities: Spark 4.0 introduces polymorphic User-Defined Table Functions (UDTFs) in Python, enabling:

  • Functions that can accept varying input schemas
  • More flexible data transformation patterns
  • Enhanced code reusability across different data structures

Spark 3.0 Baseline: While Spark 3.0 significantly improved Python support with pandas UDF redesign and Python type hints, it lacked these advanced polymorphic capabilities.

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Connectivity and Client Architecture

Spark Connect Evolution

Spark 4.0 Improvements:

  • Lightweight Python client (pyspark-client) at just 1.5 MB, dramatically reducing deployment footprint
  • Additional release tarball with Spark Connect enabled by default
  • Full API compatibility for Java clients
  • Enhanced remote connectivity patterns

Architectural Impact: These improvements enable better deployment flexibility, reduced container sizes, and simplified client-server architectures for distributed Spark applications.

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Spark 3.0 Foundation: Spark 3.0 introduced the initial Spark Connect framework but with larger client footprints and more limited language support.

Streaming and State Management

Streaming State Store Data Source

Spark 4.0 Innovation: Introduction of streaming state store as a data source provides:

  • Direct access to streaming state for debugging and monitoring
  • Better observability into streaming application state
  • Enhanced troubleshooting capabilities for complex streaming pipelines

Operational Benefits:

  • Simplified state inspection during development
  • Better production monitoring and alerting capabilities
  • Reduced debugging complexity for stateful streaming applications

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Observability and Debugging Enhancements

Structured Logging

Spark 4.0 Feature: Implementation of structured JSON logging provides:

  • Machine-readable log formats for automated processing
  • Better integration with modern logging aggregation systems
  • Enhanced debugging and monitoring capabilities
  • Improved troubleshooting workflows

Production Impact:

  • Simplified log analysis and alerting
  • Better integration with observability platforms
  • Enhanced debugging efficiency for production issues

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Performance and Optimization Considerations

Memory Management

Spark 4.0 Advantages:

  • Optimizations leveraging Java 17's improved garbage collection
  • Better memory utilization patterns with modern JVM features
  • Enhanced performance for workloads with complex data types

Spark 3.0 Performance: While Spark 3.0 achieved significant performance gains (2x improvement in TPC-DS benchmarks), it was limited by older JVM constraints and less efficient handling of semi-structured data.

Query Optimization

The VARIANT data type and improved ANSI compliance in Spark 4.0 enable more efficient query plans for mixed structured and semi-structured workloads, particularly beneficial for data lake architectures.

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Migration Considerations

Breaking Changes

ANSI Mode Default: The most significant migration consideration is the default ANSI mode behavior. Applications that previously relied on Hive-compatible null-handling behavior may require code modifications.

Java Version Requirements: Applications must be compatible with Java 17 as the default runtime, though Java 8 and 11 remain supported for transition periods.

Compatibility Strategy

Recommended Approach:

  1. Test existing applications with ANSI mode enabled in Spark 3.x environments
  2. Validate Java 17 compatibility for all dependencies
  3. Update error handling logic to accommodate stricter ANSI compliance
  4. Plan gradual adoption of new features like VARIANT data types

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Ecosystem Integration Impact

Data Lake Architecture

Spark 4.0's VARIANT data type and improved JSON handling make it significantly more suitable for modern data lake architectures where semi-structured data is prevalent. This reduces the complexity of ETL pipelines and improves query performance.

Cloud-Native Deployments

The lightweight Python client and enhanced Spark Connect capabilities make Spark 4.0 more suitable for containerized and serverless deployment patterns, aligning with modern cloud-native architectures.

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Future-Proofing Considerations

Technology Alignment

Spark 4.0's adoption of modern Java versions, structured logging, and enhanced connectivity patterns positions it well for integration with emerging data technologies and cloud services.

Development Velocity

The improved Python capabilities and enhanced debugging features in Spark 4.0 can significantly accelerate development cycles, particularly for teams working with complex data transformation pipelines.

Conclusion

Apache Spark 4.0 represents a substantial advancement over Spark 3.0, with changes that impact fundamental behavior patterns, performance characteristics, and development workflows. The default ANSI compliance, modern Java runtime, VARIANT data type, and enhanced connectivity features make it a compelling upgrade for organizations seeking improved reliability, performance, and developer productivity.

The migration from Spark 3.0 to 4.0 requires careful planning due to breaking changes, particularly around ANSI mode behavior, but the benefits in terms of data quality, performance, and developer experience make it a worthwhile investment for most organizations. The enhanced support for semi-structured data and improved observability features position Spark 4.0 as a robust platform for modern data architecture patterns.

For technical teams considering the upgrade, the key success factors include thorough testing of existing applications with ANSI mode enabled, validation of Java 17 compatibility, and strategic adoption of new features like VARIANT data types to maximize the value of the migration effort.

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What is a Data Trust Platform in financial services?
A Data Trust Platform is a unified framework that combines data observability, governance, lineage, and cataloging to ensure financial institutions have accurate, secure, and compliant data. In banking, it enables faster regulatory reporting, safer AI adoption, and new revenue opportunities from data products and APIs.
Why do AI initiatives fail in Latin American banks and fintechs?
Most AI initiatives in LATAM fail due to poor data quality, fragmented architectures, and lack of governance. When AI models are fed stale or incomplete data, predictions become inaccurate and untrustworthy. Establishing a Data Trust Strategy ensures models receive fresh, auditable, and high-quality data, significantly reducing failure rates.
What are the biggest data challenges for financial institutions in LATAM?
Key challenges include: Data silos and fragmentation across legacy and cloud systems. Stale and inconsistent data, leading to poor decision-making. Complex compliance requirements from regulators like CNBV, BCB, and SFC. Security and privacy risks in rapidly digitizing markets. AI adoption bottlenecks due to ungoverned data pipelines.
How can banks and fintechs monetize trusted data?
Once data is governed and AI-ready, institutions can: Reduce OPEX with predictive intelligence. Offer hyper-personalized products like ESG loans or SME financing. Launch data-as-a-product (DaaP) initiatives with anonymized, compliant data. Build API-driven ecosystems with partners and B2B customers.
What is data dictionary example?
A data dictionary is a centralized repository that provides detailed information about the data within an organization. It defines each data element—such as tables, columns, fields, metrics, and relationships—along with its meaning, format, source, and usage rules. Think of it as the “glossary” of your data landscape. By documenting metadata in a structured way, a data dictionary helps ensure consistency, reduces misinterpretation, and improves collaboration between business and technical teams. For example, when multiple teams use the term “customer ID”, the dictionary clarifies exactly how it is defined, where it is stored, and how it should be used. Modern platforms like Decube extend the concept of a data dictionary by connecting it directly with lineage, quality checks, and governance—so it’s not just documentation, but an active part of ensuring data trust across the enterprise.
What is an MCP Server?
An MCP Server stands for Model Context Protocol Server—a lightweight service that securely exposes tools, data, or functionality to AI systems (MCP clients) via a standardized protocol. It enables LLMs and agents to access external resources (like files, tools, or APIs) without custom integration for each one. Think of it as the “USB-C port for AI integrations.”
How does MCP architecture work?
The MCP architecture operates under a client-server model: MCP Host: The AI application (e.g., Claude Desktop or VS Code). MCP Client: Connects the host to the MCP Server. MCP Server: Exposes context or tools (e.g., file browsing, database access). These components communicate over JSON‑RPC (via stdio or HTTP), facilitating discovery, execution, and contextual handoffs.
Why does the MCP Server matter in AI workflows?
MCP simplifies access to data and tools, enabling modular, interoperable, and scalable AI systems. It eliminates repetitive, brittle integrations and accelerates tool interoperability.
How is MCP different from Retrieval-Augmented Generation (RAG)?
Unlike RAG—which retrieves documents for LLM consumption—MCP enables live, interactive tool execution and context exchange between agents and external systems. It’s more dynamic, bidirectional, and context-aware.
What is a data dictionary?
A data dictionary is a centralized repository that provides detailed information about the data within an organization. It defines each data element—such as tables, columns, fields, metrics, and relationships—along with its meaning, format, source, and usage rules. Think of it as the “glossary” of your data landscape. By documenting metadata in a structured way, a data dictionary helps ensure consistency, reduces misinterpretation, and improves collaboration between business and technical teams. For example, when multiple teams use the term “customer ID”, the dictionary clarifies exactly how it is defined, where it is stored, and how it should be used. Modern platforms like Decube extend the concept of a data dictionary by connecting it directly with lineage, quality checks, and governance—so it’s not just documentation, but an active part of ensuring data trust across the enterprise.
What is the purpose of a data dictionary?
The primary purpose of a data dictionary is to help data teams understand and use data assets effectively. It provides a centralized repository of information about the data, including its meaning, origins, usage, and format, which helps in planning, controlling, and evaluating the collection, storage, and use of data.
What are some best practices for data dictionary management?
Best practices for data dictionary management include assigning ownership of the document, involving key stakeholders in defining and documenting terms and definitions, encouraging collaboration and communication among team members, and regularly reviewing and updating the data dictionary to reflect any changes in data elements or relationships.
How does a business glossary differ from a data dictionary?
A business glossary covers business terminology and concepts for an entire organization, ensuring consistency in business terms and definitions. It is a prerequisite for data governance and should be established before building a data dictionary. While a data dictionary focuses on technical metadata and data objects, a business glossary provides a common vocabulary for discussing data.
What is the difference between a data catalog and a data dictionary?
While a data catalog focuses on indexing, inventorying, and classifying data assets across multiple sources, a data dictionary provides specific details about data elements within those assets. Data catalogs often integrate data dictionaries to provide rich context and offer features like data lineage, data observability, and collaboration.
What challenges do organizations face in implementing data governance?
Common challenges include resistance from business teams, lack of clear ownership, siloed systems, and tool fragmentation. Many organizations also struggle to balance strict governance with data democratization. The right approach involves embedding governance into workflows and using platforms that unify governance, observability, and catalog capabilities.
How does data governance impact AI and machine learning projects?
AI and ML rely on high-quality, unbiased, and compliant data. Poorly governed data leads to unreliable predictions and regulatory risks. A governance framework ensures that data feeding AI models is trustworthy, well-documented, and traceable. This increases confidence in AI outputs and makes enterprises audit-ready when regulations apply.
What is data governance and why is it important?
Data governance is the framework of policies, ownership, and controls that ensure data is accurate, secure, and compliant. It assigns accountability to data owners, enforces standards, and ensures consistency across the organization. Strong governance not only reduces compliance risks but also builds trust in data for AI and analytics initiatives.
What is the difference between a data catalog and metadata management?
A data catalog is a user-facing tool that provides a searchable inventory of data assets, enriched with business context such as ownership, lineage, and quality. It’s designed to help users easily discover, understand, and trust data across the organization. Metadata management, on the other hand, is the broader discipline of collecting, storing, and maintaining metadata (technical, business, and operational). It involves defining standards, policies, and processes for metadata to ensure consistency and governance. In short, metadata management is the foundation—it structures and governs metadata—while a data catalog is the application layer that makes this metadata accessible and actionable for business and technical users.
What features should you look for in a modern data catalog?
A strong catalog includes metadata harvesting, search and discovery, lineage visualization, business glossary integration, access controls, and collaboration features like data ratings or comments. More advanced catalogs integrate with observability platforms, enabling teams to not only find data but also understand its quality and reliability.
Why do businesses need a data catalog?
Without a catalog, employees often struggle to find the right datasets or waste time duplicating efforts. A data catalog solves this by centralizing metadata, providing business context, and improving collaboration. It enhances productivity, accelerates analytics projects, reduces compliance risks, and enables data democratization across teams.
What is a data catalog and how does it work?
A data catalog is a centralized inventory that organizes metadata about data assets, making them searchable and easy to understand. It typically extracts metadata automatically from various sources like databases, warehouses, and BI tools. Users can then discover datasets, understand their lineage, and see how they’re used across the organization.
What are the key features of a data observability platform?
Modern platforms include anomaly detection, schema and freshness monitoring, end-to-end lineage visualization, and alerting systems. Some also integrate with business glossaries, support SLA monitoring, and automate root cause analysis. Together, these features provide a holistic view of both technical data pipelines and business data quality.
How is data observability different from data monitoring?
Monitoring typically tracks system metrics (like CPU usage or uptime), whereas observability provides deep visibility into how data behaves across systems. Observability answers not only “is something wrong?” but also “why did it go wrong?” and “how does it impact downstream consumers?” This makes it a foundational practice for building AI-ready, trustworthy data systems.
What are the key pillars of Data Observability?
The five common pillars include: Freshness, Volume, Schema, Lineage, and Quality. Together, they provide a 360° view of how data flows and where issues might occur.
What is Data Observability and why is it important?
Data observability is the practice of continuously monitoring, tracking, and understanding the health of your data systems. It goes beyond simple monitoring by giving visibility into data freshness, schema changes, anomalies, and lineage. This helps organizations quickly detect and resolve issues before they impact analytics or AI models. For enterprises, data observability builds trust in data pipelines, ensuring decisions are made with reliable and accurate information.
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